Provincial dissemination of HEARTSMAP, an emergency department psychosocial assessment and disposition decision tool for children and youth
Bibliographic record
Abstract
BACKGROUND: This article describes the provincial dissemination of HEARTSMAP, an evidence-based emergency department (ED) psychosocial assessment and disposition decision tool for clinician use with children and youth. METHODS: HEARTSMAP was disseminated in partnership with local, child and youth mental health teams, as part of a quality improvement initiative implemented in British Columbia EDs. The target audience of education sessions were clinicians working in ED settings responsible for paediatric psychosocial assessments. We used the RE-AIM framework to evaluate the reach, effectiveness, adoption, implementation, and maintenance of HEARTSMAP dissemination, analyzing data from session evaluation forms and online tool data. RESULTS: Education sessions reached 475 attendees, in 52 of 95 British Columbia EDs. HEARTSMAP training was well received by clinicians with 96% describing effective content including increased comfort in conducting paediatric psychosocial assessments and confidence in disposition planning after training. Clinicians identified unclear processes and lack of local resources as the main barriers to implementation. One-third of the attendees expressed willingness to use the tool, and 27% of registered clinicians have used the tool postimplementation. CONCLUSIONS: Our approach reached and effectively trained clinicians from over half of the province's EDs to use HEARTSMAP for emergency paediatric psychosocial assessments. For some, this provided greater comfort and confidence for these assessments and the following disposition decisions. This evaluation provides valuable insights on training clinicians to use a paediatric mental health tool within diverse ED settings and emphasized the need for ongoing support and institutional engagement to facilitate local, infrastructural, and operational processes for adoption and maintenance, postdissemination.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".